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Word Embeddings for Wine Recommender Systems Using Vocabularies of Experts and Consumers

Abstract : This vision paper proposes an approach to use the most advanced word embeddings techniques to bridge the gap between the discourses of experts and non-experts and more specifically the terminologies used by the two communities. Word embeddings makes it possible to find equivalent terms between experts and non-experts, by approach the similarity between words or by revealing hidden semantic relations. Thus, these controlled vocabularies with these new semantic enrichments are exploited in a hybrid recommendation system incorporating content-based ontology and keyword-based ontology to obtain relevant wines recommendations regardless of the level of expertise of the end user. The major aim is to find a non-expert vocabulary from semantic rules to enrich the knowledge of the ontology and improve the indexing of the items (i.e. wine) and the recommendation process.
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Contributor : Laurent Gautier <>
Submitted on : Tuesday, September 11, 2018 - 6:42:38 PM
Last modification on : Tuesday, November 10, 2020 - 4:49:25 PM
Long-term archiving on: : Wednesday, December 12, 2018 - 3:51:09 PM


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  • HAL Id : halshs-01872273, version 1


Christophe Cruz, Cyril Nguyen Van, Laurent Gautier. Word Embeddings for Wine Recommender Systems Using Vocabularies of Experts and Consumers. Open Journal of Web Technologies, RonPub, 2018, Special Issue: Proceedings of the International Workshop on Web Data Processing & Reasoning (WDPAR 2018) in conjunction with the 41st German Conference on Artificial Intelligence, 5 (1), pp.23-30. ⟨halshs-01872273⟩



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